Lane network data generation apparatus and storage medium

ABSTRACT

An all-one-way network data for autonomous driving is generated according to traffic information of a road data included in a navigation map data. The lane network data for autonomous driving is generated based on the all-one-way network data according to information of a numerical number of lanes in the road data. A virtual lane boundary data is generated based on the all-one-way network data. A position of the lane network data and a position of the virtual lane boundary data are corrected according to a basic road map data and an aerial photograph data.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of InternationalPatent Application No. PCT/JP2018/035383 filed on Sep. 25, 2018, whichdesignated the U.S. and claims the benefit of priority from JapanesePatent Application No. 2017-232591 filed on Dec. 4, 2017. The entiredisclosures of all of the above applications are incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to a lane network data generation deviceand a storage medium.

BACKGROUND

There is a method of measuring the shape and position of a road withhigh accuracy using a dedicated moving vehicle and generating lanenetwork data for autonomous driving. This method may be expensive ingeneral because it requires enormous work by expensive sensors andhumans. Therefore, lane network data is generated only in a limited areasuch as an expressway or a limited highway. It is not easy to generatenationwide lane network data including general roads using theabove-described method.

SUMMARY

According to an example embodiment, an all-one-way network data forautonomous driving is generated according to traffic information of aroad data included in a navigation map data. The lane network data forautonomous driving is generated based on the all-one-way network dataaccording to information of a numerical number of lanes in the roaddata. A virtual lane boundary data is generated based on the all-one-waynetwork data. A position of the lane network data and a position of thevirtual lane boundary data are corrected according to a basic road mapdata and an aerial photograph data.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentdisclosure will become more apparent from the following detaileddescription made with reference to the accompanying drawings. In thedrawings:

FIG. 1 is a functional block diagram showing one embodiment;

FIG. 2 is a flowchart;

FIG. 3 is a diagram (part 1) illustrating a feature of generatingall-one-way network data;

FIG. 4 is a diagram (part 2) illustrating a feature of generatingall-one-way network data;

FIG. 5 is a diagram showing a connection between all-one-way networkdata and an intersection;

FIG. 6 is a diagram (part 1) illustrating a feature of generating lanenetwork data;

FIG. 7 is a diagram (part 2) illustrating a feature of generating lanenetwork data;

FIG. 8 is a diagram (part 3) illustrating a feature of generating lanenetwork data;

FIG. 9 is a diagram (part 4) illustrating a feature of generating lanenetwork data;

FIG. 10 is a diagram (part 1) illustrating a feature of generatingvirtual lane boundary data;

FIG. 11 is a diagram (part 2) illustrating a feature of generatingvirtual lane boundary data;

FIG. 12 is a diagram showing a feature of adding additional lane data;

FIG. 13 is a diagram showing a feature of generating driving trajectorydata within an intersection;

FIG. 14 is a diagram (part 1) illustrating a feature for correcting theposition of lane network data;

FIG. 15 is a diagram (part 2) illustrating a feature of correcting theposition of lane network data;

FIG. 16 is a diagram (part 3) illustrating a feature of correcting theposition of lane network data; and

FIG. 17 is a diagram (part 4) illustrating a feature of correcting theposition of lane network data.

DETAILED DESCRIPTION

A conceivable technique discloses a method of collecting front imagescaptured by a camera mounted on a general vehicle and generating lanenetwork data without using a dedicated moving vehicle. Further, forexample, another conceivable technique discloses a technique forimproving the accuracy of navigation map data using aerial photographdata. Further, for example, another conceivable technique discloses amethod of recognizing a road center line or a road outside line usingaerial photograph data, correcting the position of the lane networkdata, and improving the accuracy of the lane network data.

The above method provides to expand the range in which the lane networkdata is generated. However, the method is similar to the method using adedicated moving vehicle in view of the feature that it is necessary togo to an actual place along a road and to photograph the state of theroad. Therefore, there may be a difficulty that the lane network datacannot be generated for a road that has not been visited. Further, theabove other method may include a difficulty that the accuracy of the mapdata for navigation may be only improved, and the accuracy of the lanenetwork data may not be improved. Furthermore, although the above othermethod can provide to eliminate the necessity to go to the actual place,the aerial photograph may be unclear or vehicles on the road may bephotographed, so that the road center line and the road outside line maynot be captured. Thus, in this case, there may be a difficulty that theaccuracy of the aerial photograph data decreases and the accuracy of thelane network data cannot be increased.

A lane network data generation device and a storage medium are providedsuch that it can appropriately generate high-accuracy lane network datafor autonomous driving without going to an actual place.

According to an example embodiment of the present disclosure, theall-one-way network data generation unit generates all-one-way networkdata using the traffic information of the road data included in thenavigation map data. The lane network data generation unit generateslane network data for autonomous driving from the all-one-way networkdata using the information of the number of lanes in the road data. Thevirtual lane boundary data generation unit generates virtual laneboundary data from the all-one-way network data. The position correctionunit corrects the position in the lane network data and the position inthe virtual lane boundary data using the basic road map data and theaerial photograph data.

That is, different from the conceivable method in which the position ofthe lane network data is corrected using only the aerial photographdata, the correction is performed using the basic road map data inaddition to the aerial photograph data. By using the basic road mapdata, the information about lanes is not acquired, but it is possible toroughly obtain the total width of all lanes and roughly estimate thewidth of each lane, and roughly correct the position of the lane networkdata. Further, by using the aerial photograph data, the position of thelane network data can be corrected in detail. This makes it possible toappropriately generate high-accuracy lane network data for autonomousdriving while avoiding to go to the actual place.

Hereinafter, an embodiment will be described with reference to thedrawings. As shown in FIG. 1 , a lane network data generation apparatus1 is a generation device that generates lane network data for autonomousdriving, and includes an all-one-way network data generator 2, a lanenetwork data generator 3, a virtual lane boundary data generator 4, anadditional lane data adder 5, an intersection driving trajectory datagenerator 6, a first position correction unit 7, and a second positioncorrection unit 8. The lane network data for autonomous driving is dataindicating a trajectory when the vehicle travels under autonomousdriving operation. These functional blocks are provided by amicrocomputer having a CPU (Central Process Unit), a ROM (Read OnlyMemory), a RAM (Random Access Memory), and an I-O (Input-Output) device.The microcomputer executes a process corresponding to the computerprogram by performing the computer program stored in the non-transitorytangible storage medium, and controls the overall operations of the lanenetwork data generation apparatus 1. The computer program executed bythe microcomputer include a lane network data generation program.

The all-one-way network data generator 2 reads the navigation map datastored in the navigation map data storage 9, and generates theall-one-way network data using the traffic information of the road dataincluded in the read navigation map data. The traffic information isinformation indicating whether the traffic is one-way or two-way. Aftergenerating the all-one-way network data, the all-one-way network datagenerator 2 stores the generated all-one-way network data in theall-one-way network data storage 10.

The lane network data generator 3 reads the all-one-way network datastored in the all-one-way network data storage 10, and generates thelane network data from the read all-one-way network data using thenumber-of-lanes information of the road data included in the navigationmap data. The number-of-lanes information is information indicating thenumber of lanes. After generating the lane network data, the lanenetwork data generator 3 stores the generated lane network data in thelane network data storage 11.

The virtual lane boundary data generator 4 reads the all-one-way networkdata stored in the all-one-way network data storage 10, and generatesthe virtual lane boundary data from the read all-one-way network data.The virtual lane boundary data is data indicating a virtual boundary ofthe lane network data. When generating the virtual lane boundary data,the virtual lane boundary data generator 4 stores the generated virtuallane boundary data in the virtual lane boundary data storage 12.

The additional lane data adder 5 reads the navigation map data stored inthe navigation map data storage 9 and the lane network data stored inthe lane network data storage 11. The additional lane data adder 5 addsthe additional lane data to the lane network data using the lane typeinformation of the road data included in the navigation map data. Thelane type information is information indicating whether the lane is anormal lane or an additional lane. The additional lane is a lanetemporarily increased mainly before an intersection. When the additionallane data is added to the lane network data, the additional lane dataadder 5 reflects information about the added additional lane data on thelane network data stored in the lane network data storage 11. Further,the additional lane data adder 5 reflects the information about theadded additional lane data on the virtual lane boundary data stored inthe virtual lane boundary data storage 12 and the intersection drivingtrajectory data stored in the intersection driving trajectory datastorage 13.

The intersection driving trajectory data generator 6 reads thenavigation map data stored in the navigation map data storage 9 and thelane network data stored in the lane network data storage 11. Theintersection driving trajectory data generator 6 generates theintersection driving trajectory data using the intersection connectioninformation of the road data included in the navigation map data withrespect to the lane network data. The intersection connectioninformation is information indicating a connection manner of theintersection. When generating the intersection driving trajectory datawith respect to the lane network data, the intersection drivingtrajectory data generator 6 stores the generated intersection drivingtrajectory data in the intersection driving trajectory data storage 13.The intersection driving trajectory data generator 6 reflects theinformation relating to the generated intersection driving trajectorydata on the lane network data stored in the lane network data storage 11and the virtual lane boundary data stored in the virtual lane boundarydata storage 12.

The first position correction unit 7 reads the basic road map datastored in the basic road map data storage 14, the lane network datastored in the lane network data storage 11, and the virtual laneboundary data stored in the virtual lane boundary data storage 12. Thebasic road map data stored in the basic road map data storage 14 is, forexample, digitized basic road map data issued by the GeospatialInformation Authority of Japan.

The first position correction unit 7 corrects the position of the lanenetwork data and the position of the virtual lane boundary data by usingthe road edge information and the road component line information of theread basic road map data. The road edge information is informationindicating the range of the road, and the road component lineinformation is information indicating the presence of a sidewalk, acenter median strip, or the like within the range of the road. When thefirst position correction unit 7 corrects the position of the lanenetwork data and the position of the virtual lane boundary data, thefirst position correction unit 7 reflects the corrected informationrelating to the position of the lane network data on the lane networkdata stored in the lane network data storage 11. The first positioncorrection unit 7 reflects the corrected information about the virtuallane boundary data on the virtual lane boundary data stored in thevirtual lane boundary data storage 12. The first position correctionunit 7 also reflects the information about the corrected position of thelane network data and the corrected position of the virtual laneboundary data on the intersection driving trajectory data stored in theintersection driving trajectory data storage 13.

The second position correction unit 8 reads the aerial photograph datastored in the aerial photograph data storage 15, the lane network datastored in the lane network data storage 11, and the virtual laneboundary data stored in the virtual lane boundary data storage 12. Theaerial photograph data stored in the aerial photograph data storage 15is, for example, digitized data of the image by photographing a groundwith a camera mounted on an aircraft.

The second position correction unit 8 corrects the position of the lanenetwork data and the position of the virtual lane boundary data usingthe paint information of the read aerial photograph data. When thesecond position correction unit 8 corrects the position of the lanenetwork data and the position of the virtual lane boundary data, thesecond position correction unit 8 reflects the corrected informationrelating to the position of the lane network data on the lane networkdata stored in the lane network data storage 11. The second positioncorrection unit 8 reflects the corrected information about the virtuallane boundary data on the virtual lane boundary data stored in thevirtual lane boundary data storage 12. The second position correctionunit 8 also reflects the information about the corrected position of thelane network data and the corrected position of the virtual laneboundary data on the intersection driving trajectory data stored in theintersection driving trajectory data storage 13.

A function of the above configuration will be described with referenceto FIGS. 2 to 17 .

The lane network data generation apparatus 1 starts executing the lanenetwork data generation process when the start event of the lane networkdata generation process is established. Hereinafter, the processexecuted by each functional block in the lane network data generationapparatus 1 will be described.

First, the all-one-way network data generator 2 generates theall-one-way network data using the traffic information of the road dataincluded in the navigation map data (at S1, corresponding to anall-one-way network data generation procedure). That is, the all-one-waynetwork data generator 2 determines whether the road data indicatesone-way or two-way. When determining that the road data indicatesone-way, the all-one-way network data generator 2 does not process theroad data represented by the single line as it is, as shown in FIG. 3 ,and generates the all-one-way network data.

When determining that the road data indicates two-way traffic, theall-one-way network data generator 2 separates the road data representedby the single line into an upward direction line and a downwarddirection line, as shown in FIG. 4 , and duplicates the single line intotwo lines to generate the all-one-way network data. In this case, theall-one-way network data generator 2 provides to set the original roaddata at a center position and to set the all-one-way network data at aposition shifted from the original road data in parallel to the originalroad data by a predetermined distance (for example, 1.5 meters) on bothleft and right sides of the road data. The all-one-way network datagenerator 2 provides to set the all-one-way network data with a drivingdirection on the left side with respect to the direction of the roaddata when a left-hand traffic system is established in an area, and toset the all-one-way network data with a driving direction on the rightside with respect to the direction of the road data when a right-handtraffic system is established in an area. The all-one-way network datagenerator 2 simply shifts a straight line section in parallel to theoriginal road data on both sides of the road data in a case where theoriginal road data indicates the straight line section. The all-one-waynetwork data generator 2 executes a process for setting the distancebetween the road data and each line of the all-one-way network dataincluding the upward direction line and the downward direction line tobe equal to each other in addition to the process for shifting the curvesection of the all-one-way network data in parallel to the original roaddata on both sides of the road data in a case where the original roaddata indicates the curve section.

In the road data, the road is represented by a single line, and theintersection is represented by a single point. Therefore, four lines atthe crossroad intersection are connected to the single point of theintersection. Therefore, as shown in FIG. 5 , it is necessary for theall-one-way network data generator 2 to execute the process for editinga part of the intersection when duplicating the road data represented bythe single line into two lines. Only information indicating to whichintersection the one-way network data is connected is stored. Inaddition, the all the one-way network data generator 2 stores anattribute that the road is a special road when the driving direction ofthe road changes depending on the time zone in a case where the roaddata represented by the single line is duplicated into two lines.

Next, the lane network data generator 3 generates the lane network datafrom all-one-way network data using the number-of-lanes information ofthe road data included in the navigation map data (at S2, correspondingto a lane network data generation procedure). That is, the lane networkdata generator 3 copies the all-one-way network data according to thenumber of lanes, and sets the lane network data at a position shifted bya predetermined distance (for example, 3 meters). When the road dataincludes width information, the lane network data generator 3 maycalculate the distance for each lane based on the width information.

The lane network data generator 3 sets the lane network data dependingon a feature whether the data is the all-one-way network data notprepared by duplicating the road data or the all-one-way network dataprepared by duplicating the road data. That is, as shown in FIGS. 6 and7 , the lane network data generator 3 sets the lane network data so asto arrange the position of the road data at the center when determiningthat the original road data indicates one-way and the all-one-waynetwork data that is not prepared by duplicating the road data. When thenumber of lanes is “1”, the lane network data generator 3 sets the lanenetwork data at the same position as the all-one-way network data asshown in FIG. 6 . When the number of lanes is “2”, the lane network datagenerator 3 sets the lane network data at a position shifted from theall-one-way network data as shown in FIG. 7 .

When the lane network data generator 3 determines that the original roaddata indicates two-way traffic and indicates the all-one-way networkdata obtained by duplicating the road data as shown in FIGS. 8 and 9 ,the lane network data generator 3 sets the position of the road data atthe center and sets the lane network data with a driving direction onthe left side with respect to the direction of the road data when aleft-hand traffic system is established in an area, and sets the lanenetwork data with a driving direction on the right side with respect tothe direction of the road data when a right-hand traffic system isestablished in an area. When the number of lanes for each direction is“1”, the lane network data generator 3 sets the lane network data at thesame position as the all-one-way network data for each direction asshown in FIG. 8 . When the number of lanes for each direction is “2”,the lane network data generator 3 sets the lane network data the sameposition as the all-one-way network data for each direction and at theposition shifted from the all-one-way network data as shown in FIG. 9 .Also in this case, the lane network data generator 3 only storesinformation to which intersection the lane network data is connected. Inthis case as well, the lane network data generator 3 performs a processof setting the interval between the original road data and each lane ofthe lane network data to be equal to each other in a case where theoriginal road data indicates the curve section. Also, as shown in FIG.11 , the lane network data generator 3 sets two lines including upperand lower lanes in the lane network data side by side without increasingthe distance therebetween when the driving direction of the road changesdepending on the time zone.

Next, the virtual lane boundary data generator 4 generates the virtuallane boundary data from the all-one-way network data using thenumber-of-lanes information of the road data included in the navigationmap data (at S3, corresponding to a virtual lane boundary datageneration procedure). That is, as shown in FIG. 10 , when the lanenetwork data is arranged at a position shifted by a predetermineddistance (for example, 3 meters) as described above, the virtual laneboundary line data generator 4 generates the virtual lane boundary dataarranged at a position shifted by a predetermined distance (for example,1.5 meters) on both left and right sides of the lane network data. Inthis case as well, the virtual lane boundary data generator 4 performs aprocess of setting the interval between the original road data and eachboundary of the virtual lane boundary data to be equal to each other ina case where the original road data indicates the curve section. Also,as shown in FIG. 11 , the virtual lane boundary line data generator 4sets the virtual lane boundary data at a position shifted by apredetermined distance (for example, 1.5 meters) on both right and leftsides of each of two lines of the lane network data arranged side byside without increasing the distance therebetween when the drivingdirection of the road changes depending on the time zone.

Next, the additional lane data adder 5 adds the additional lane data tothe lane network data using the lane type information of the road dataincluded in the navigation map data (at S4, corresponding to theadditional lane data adding procedure). That is, for example, in an areawhere a road traffic system of a left-hand traffic is implemented, whena waiting situation for an oncoming vehicle occurs during a right turn,an additional lane may be disposed as a place where a right-turn vehiclestands by so as not to obstruct the traffic of a vehicle going straight.In the navigation map data, the additional lane data may not bedigitized accurately. On the other hand, the information indicating theconnection relationship for each lane may be provided for the roadsentering into and leaving the intersection as guiding information forturning right and left at the intersection. In some cases, theinformation may include lane type information indicating whether eachlane is a normal lane or an additional lane.

The additional lane data adder 5 determines whether the informationincludes the additional lane data before the intersection. Whendetermining that the information does not include the additional lanedata, the adder 5 does not add the additional lane data. Whendetermining that the information includes the additional lane data, theadder 5 adds the additional lane data. As shown in FIG. 12 , theadditional lane data adder 5 sets the distance between the lane networkdata and the additional lane data to be a predetermined distance (forexample, 3 meters), and sets a branch point from the original lanenetwork data at a position spaced apart from the end point of theintersection by a distance (for example, 20 meters). When the additionallane data is added and overlaps with the lane network data of theoncoming lane by adding the additional lane data, the additional lanedata adder 5 shifts the lane network data of the oncoming lane to adirection for avoiding the overlapping of the lane network data as awhole. When the additional lane data is added to the lane network data,the additional lane data adder 5 reflects information on the addedadditional lane data on the lane network data, the virtual lane boundarydata, and the driving trajectory data within the intersection.

Next, the intersection driving trajectory data generator 6 generates theintersection driving trajectory data for the lane network data using theintersection connection information of the road data included in thenavigation map data (at S5, corresponding to the intersection drivingtrajectory data generation procedure). That is, in the navigation mapdata, as the connection relationship for each lane of the intersection,for example, the information of the first lane as the leftmost laneindicates the left turn and going straight, the information of thesecond lane adjacent to the first lane indicates the going straight, andthe information of the third lane adjacent to the second lane indicatesthe right turn. However, for example, when the road ahead of the leftturn includes two lanes, the information does not indicate of which lanethe vehicle enters. As shown in FIG. 13 , the driving trajectory datagenerator 6 generates the intersection driving trajectory data from thedriving trajectory data for connecting the lane entering theintersection and all lanes ahead of the intersection that may exit theintersection with respect to the entering lane. FIG. 13 shows an exampleof the intersection driving trajectory data for connecting the lane“WI1” entering the intersection from the west and each of the lanes“NO1” and “NO2” exiting to the north of the intersection, the lane “EO1”exiting to the east of the intersection, and the lanes “SO1” and “SO2”exiting to the south of the intersection. The intersection drivingtrajectory data generator 6 generates the intersection drivingtrajectory data for all lanes entering the intersection. When theintersection driving trajectory data generator 6 generates theintersection driving trajectory data for the lane network data, thegenerator 6 generates the information of the generated intersectiondriving trajectory data on the lane network data and the virtual laneboundary data.

Next, the first position correction unit 7 corrects the position of thelane network data and the position of the virtual lane boundary datausing the road edge information and the road component line informationof the basic road map data (at S6, corresponding to the first positioncorrection procedure). That is, the first position correction unit 7corrects the position by overlaying the lane network data with the roadedge information and the road component line information. The advantageof using the basic road map data is that the basic road map data canacquire information that cannot be observed from the sky and is notincluded in the aerial photograph data. That is, it is possible toacquire information of an underground road or a road shielded by anotherstructure. As shown in FIG. 14 , when only one line in the lane networkdata exists on a road indicated by a road edge or a road constituentline, the first position correction unit 7 corrects the position of thelane network data at the center of the road.

As shown in FIGS. 15 and 16 , when a plurality of lines in the lanenetwork data exists on a road indicated by a road edge or a roadconfiguration line, the first position correction unit 7 divides thewidth of the road equally into the number of lanes, and corrects theposition of the lane network data at the center of each equally-dividedlane. That is, as shown in FIG. 15 , when the road width is relativelynarrow with respect to the number of lines in the lane network data, thefirst position correction unit 7 corrects the position of the lanenetwork data so as to set the distance between the lines in the lanenetwork data to be narrower than the above-described predetermineddistance (for example, 3 meters). As shown in FIG. 16 , when the widthof the road is relatively large with respect to the number of lines inthe lane network data, the first position correction unit 7 corrects theposition of the lane network data so as to set the distance between thelines in the lane network data to be wider than the above-describedpredetermined distance (for example, 3 meters). In this case, the firstposition correction unit 7 corrects the position of the lane networkdata so that the distance between the road edge and the outside line ofthe lane network data is approximately a half of the distance betweenlines in the lane network data. When the first position correction unit7 corrects the position of the lane network data, the unit 7 alsocorrects the position of the virtual lane boundary data.

Next, the second position correction unit 8 corrects the position of thelane network data and the position of the virtual lane boundary datausing the paint information of the aerial photograph data (at S7,corresponding to a second position correction procedure). That is, thesecond position correction unit 8 corrects the position of the lanenetwork data according to the paint information of the aerial photographdata. The advantage of using aerial photograph data is to obtaininformation not included in the basic road map data (such as a roadcenter line, a lane boundary line, a road boundary line, a divergencezone, a pedestrian cross walk, a stop line, a traffic direction arrowaccording to the direction of traffic, etc.).

Since the first position correction unit 7 adjusts the position of thelane network data and the position of the virtual lane boundary data tobe closer to the actual positions, the second position corrector 8 canlimits an area for extracting information to be recognized from theaerial photograph data. Thus, the process time can be reduced, and theprobability of detecting unnecessary information by mistake can bereduced. When recognizing, for example, a lane boundary line, a roadboundary line, and a road center line based on the aerial photographdata, the second position correction unit 8 sets the position of thevirtual lane boundary line data at the center, and recognizes each linebased on the surrounding image. Further, when recognizing the trafficsegment arrow for each traffic direction based on the aerial photographdata, the second position correction unit 8 sets the lane network dataas the center and recognizes the arrow based on the surrounding image.

The second position correction unit 8 corrects the position of thevirtual lane boundary data using the positions of the lane center line,the lane boundary line, the road boundary line, the divergence zone andthe like recognized from the aerial photograph data, and corrects theposition of the lane network data at the center of each lane sandwichedbetween the corrected virtual lane boundary line data. When the roaddoes not include the road center line since the width of the road is notsufficiently wide although the road is a two-way road, the lane networkdata indicates two lines of the upper and lower lines in a single lane.Therefore, as shown in FIG. 17 , the second position correction unit 8sets two lines of the lane network data into the single lane withmaintaining a predetermined distance so that vehicles pass each other,and deletes the corresponding virtual lane boundary data.

Further, the second position correction unit 8 corrects the position ofthe additional lane data from the corrected position of the virtual laneboundary data corrected based on the aerial photograph data. That is,when the width of the road is not enough to arrange a right-turn onlylane or a left-turn only lane, the second position correction unit 8 maywiden the width of the lane before the intersection and handle thesingle lane as if it were two lanes in some cases. In this case,although the road includes the single lane observed from the aerialphotograph data, it is necessary to generate the lane network data withseparating into two lanes. In such a place, generally, a traffic segmentarrow for each traveling direction is displayed in parallel in one lane.When the second position correction unit 8 detects such a place from theaerial photograph data, the unit 8 separates the lane network data intotwo lanes from the point where the lane is widened, and generates asindependent lane network data. Here, when the lane network data and thevirtual lane boundary data generated from the road data are notconsistent with the lane boundary line, the road boundary line, thedivergence zone, the lane center line, and the like recognized from theaerial photograph data, the second position correction unit 8prioritizes the information recognized from the aerial photograph data,and corrects the lane network data and the virtual lane boundary data.

The lane network data generation apparatus 1 performs the series ofprocesses described above, and ends the lane network data generationprocess.

The embodiment described above may provide effects as below.

In the lane network data generation apparatus 1, the position of thelane network data and the position of the virtual lane boundary data arecorrected using the basic road map data and the aerial photograph data.Different from a conceivable method of correcting the position of thelane network data using only the aerial photograph data, the total widthof all lanes is roughly obtained and the width of each lane is roughlyestimated using the basic road map data in addition to the aerialphotograph data. Thus, the position of the lane network data can beroughly corrected. Further, by using the aerial photograph data, theposition of the lane network data can be corrected in detail. Bycorrecting the position of the lane network data using the basic roadmap data and the aerial photograph data together, it is possible toappropriately generate high-accuracy lane network data for autonomousdriving while eliminating the necessity to go to the actual place.

Further, in the lane network data generation apparatus 1, the additionallane data is added to the lane network data using the lane typeinformation of the road data. By adding the additional lane data, it ispossible to appropriately generate high-accuracy lane network data forautonomous driving even for the lane temporarily increased before theintersection.

Further, the lane network data generation apparatus 1 generates theintersection driving trajectory data using the intersection connectioninformation of the road data with respect to the lane network data. Bygenerating the intersection driving trajectory data, it is possible toappropriately generate high-accuracy lane network data for autonomousdriving even within an intersection.

Although the present disclosure has been described in accordance withthe embodiments, it is understood that the present disclosure is notlimited to the embodiments and structures. The present disclosure maycover various modification examples and equivalent arrangements.Furthermore, various combinations and formations, and other combinationsand formations including one, more than one or less than one element maybe included in the scope and the spirit of the present disclosure.

In the lane network data generation apparatus 1, each functional blockmay be distributed. That is, for example, a part of the functionalblocks may be provided in a server different from the lane network datageneration apparatus 1, and various types of data may be transmitted andreceived via a communication system to generate the lane network data orto correct the position of the lane network data.

The controllers and methods described in the present disclosure may beimplemented by a special purpose computer created by configuring amemory and a processor programmed to execute one or more particularfunctions embodied in computer programs. Alternatively, the controllersand methods described in the present disclosure may be implemented by aspecial purpose computer created by configuring a processor provided byone or more special purpose hardware logic circuits. Alternatively, thecontrollers and methods described in the present disclosure may beimplemented by one or more special purpose computers created byconfiguring a combination of a memory and a processor programmed toexecute one or more particular functions and a processor provided by oneor more hardware logic circuits. The computer programs may be stored, asinstructions being executed by a computer, in a tangible non-transitorycomputer-readable medium.

It is noted that a flowchart or the processing of the flowchart in thepresent application includes sections (also referred to as steps), eachof which is represented, for instance, as S1. Further, each section canbe divided into several sub-sections while several sections can becombined into a single section. Furthermore, each of thus configuredsections can be also referred to as a device, module, or means.

While the present disclosure has been described with reference toembodiments thereof, it is to be understood that the disclosure is notlimited to the embodiments and constructions. The present disclosure isintended to cover various modification and equivalent arrangements. Inaddition, while the various combinations and configurations, othercombinations and configurations, including more, less or only a singleelement, are also within the spirit and scope of the present disclosure.

What is claimed is:
 1. A system comprising: a lane network datageneration apparatus that generates lane network data for autonomousdriving, the lane network data generation apparatus comprising: aprocessor, wherein the processor is configured to: generate all-one-waynetwork data based on traffic information in road data included innavigation map data; generate the lane network data for autonomousdriving based on the all-one-way network data based on a number of lanesin the road data, the lane network data including a trajectory thevehicle follows during autonomous driving; generate virtual laneboundary data based on the all-one-way network data, the virtual laneboundary data including boundaries for the trajectory; and correct aposition of the trajectory in the lane network data and a position ofthe boundaries in the virtual lane boundary data based on basic road mapdata and aerial photograph data; a server and communication systemconfigured to receive the corrected lane network data and the correctedvirtual lane boundary data from the lane network data generationapparatus; and a plurality of autonomous vehicles configured to receivethe corrected lane network data and the corrected virtual lane boundarydata from the server and communication system, each autonomous vehiclebeing configured to perform autonomous driving of the autonomousvehicles based on the corrected lane network data and the correctedvirtual lane boundary data.
 2. The system recited by claim 1, wherein:at least one of the generating of the all-one-way network data, thegenerating of the lane network data for autonomous driving, thegenerating of the virtual lane boundary data, and the correcting of theposition of the trajectory in the lane network data and the position ofthe boundaries in the virtual lane boundary data is performed in realtime.
 3. The system according to claim 1, wherein the processor isfurther configured to add additional lane data to the lane network databased on lane type information in the road data.
 4. The system accordingto claim 1, wherein the processor is further configured to: generateintersection driving trajectory data based on intersection connectioninformation with respect to the lane network data.
 5. The systemaccording to claim 1, wherein: the processor is further configured to:correct the position of the trajectory in the lane network data and theposition of the boundaries in the virtual lane boundary data based onroad edge information and road component line information in the basicroad map data; and correct the position of the trajectory in the lanenetwork data and the position of the boundaries in the virtual laneboundary data based on paint information in the aerial photograph data.6. The system of claim 1, wherein a trajectory that each of theplurality of autonomous vehicles follows during autonomous driving iscontrolled based on the corrected lane network data and the correctedvirtual lane boundary data.
 7. The system of claim 1, wherein theprocessor is further configured to generate the lane network dataincluding the trajectory the vehicle follows during autonomous drivingand the virtual lane boundary data including the boundaries for thetrajectory with an accuracy higher than a predetermined accuracy forautonomous driving without being physically present at a road todetermine the trajectory and the boundaries on the road.
 8. The systemof claim 1, wherein, a total width of all lanes and a width of each laneis estimated based on the basic road map data to correct the position ofthe trajectory in the lane network data, and the position of thetrajectory in the lane network data is corrected based on the aerialphotograph data.